CN105349643B - The method and microRNAs markers that serotonin changes after prediction sleep deprivation - Google Patents
The method and microRNAs markers that serotonin changes after prediction sleep deprivation Download PDFInfo
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Abstract
The present invention provides under sleep deprivation 5 hydroxytryptamines (5 HT) correlation microRNAs markers combine：4701 3p and hsa miR of hsa miR, 4800 5p, and predict the method that 5 hydroxytryptamines (5 HT) change under sleep deprivation and the method that prediction sleep deprivation rings the shadow using it, and provide and detect its kit.
Description
Technical field
The invention belongs to physiology field, the physiological change after sleep deprivation is particularly related to.
Background technology
For sleep to Cognitive Function studies have shown that continuous two every night the sleep restriction less than 6 hours can lead
The reduction of cognitive function is caused, including the reaction time extends, errors increase in simple reaction task, ability of mental arithmetic reduces and work
Make impaired memory.Since biological rhythm widely regulates and controls various physiological functions, hormone secretion, behavior, cognitive function etc., people's
Physiological function and work capacity can not avoid the regulation and control by inside of human body biological rhythm to reach rebalancing, the imbalance of biological rhythm
The health and operational capacity of people can be seriously affected.
In the Modeling Research of sleep rhythm disorder, U.S. Van Dongen are taught in classics " double process model (two-
Process model) " on the basis of it is further noted that individual to sleep missing sensitivity differences be modeling in it is very important
An important factor for, and propose the rhythm disorder and ability to work prediction model of individuation.
Serotonin (5-HT) variation is important sleep deprivation physiological effect.The correlation of blood platelet and sleep deprivation is early
Have been reported that, molecular mechanism it is related with serotonin (Heiser et al., 1997；Schreiber et al.,1997).Have
Document shows that sleep deprivation can stimulate the release (Grossman et al., 2000) of serotonin.Serotonin is shelled with sleep
The body reaction taken by force is closely related, and the sleep deprivation metabolism group delivered recently is studies have shown that serotonin, tryptophan, ox sulphur
27 kinds of metabolins such as acid significantly rise (Davies et al.2014) after sleep deprivation.
In conclusion screening can characterize and predict sleep deprivation physiological effect, especially serotonin (5-HT) variation
The epigenetics index of individual difference is important.
Invention content
The present inventor uses the human experimentation model of sleep deprivation, and sleep rhythm individuation mould can be characterized by screening and identifying
The epigenetics index of shape parameter.
On the one hand, the present invention provides serotonin (5-HT) the correlation microRNAs markers under sleep deprivation
Hsa-miR-4701-3p and hsa-miR-4800-5p, their sequence are SEQ ID NO.1 and/or SEQ ID NO.2 respectively.
Preferably, the microRNAs markers are also selected from following sequence and/or its complementary series：SEQ ID NO.1+SEQ
ID NO.2, SEQ ID NO.2+SEQ ID NO.1, SEQ ID NO.1+SEQ ID NO.2+SEQ ID NO.1 and SEQ ID
NO.2+SEQ ID NO.1+SEQ ID NO.2。
SEQ ID NO.1 and SEQ ID NO.2 can characterize the serotonin (5-HT) under sleep deprivation.Serotonin
The relationship of (5-HT), hsa-miR-4701-3p and hsa-miR-4800-5p meet Y=ax1+bx2+c, and Y is serotonin (5-
HT variation), x1 and x2 are miRNA variables hsa-miR-4701-3p and hsa-miR-4800-5p respectively, and a and b are variables
Regression coefficient, c are constants, preferably a:b:C=(- 1):(- 12 to -8):(20 to 30)；For example, being -0.1, -1 and 2.4 respectively.
On the other hand, the present invention provides the method that serotonin (5-HT) under a kind of prediction sleep deprivation changes, institutes
The method of stating includes detecting the level of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p expression, hsa-
The sequence of miR-4701-3p and hsa-miR-4800-5p is SEQ ID NO.1 and SEQ ID NO.2 respectively.High expression level
Hsa-miR-4701-3p and hsa-miR-4800-5p indicates the reduction of serotonin (5-HT) level after sleep deprivation.At one
In specific embodiment, it is miRNA variables hsa-miR- respectively that serotonin (5-HT), which changes Y=ax1+bx2+c, x1 and x2,
4701-3p and hsa-miR-4800-5p, a and b are the regression coefficients of variable, and c is constant, preferably a:b:C=(- 1):(- 12 to-
8):(20 to 30)；Such as respectively it is -0.1, -1 and 2.4.
It yet still another aspect, the present invention provides a kind of method that prediction sleep deprivation rings the shadow, the method includes inspections
Survey the level of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p expression, hsa-miR-4701-3p and
The sequence of hsa-miR-4800-5p is SEQ ID NO.1 and SEQ ID NO.2 respectively.The hsa-miR-4701- of high expression level
3p and hsa-miR-4800-5p indicates the reduction of serotonin (5-HT) level after sleep deprivation.
In another aspect, the present invention also provides detection microRNAs markers hsa-miR-4701-3p and hsa-miR-
The horizontal kit of 4800-5p expression, the kit includes sequence chosen from the followings and/or its complementary series：hsa-
MiR-4701-3p and hsa-miR-4800-5p；SEQ ID NO.1+SEQ ID NO.2；SEQ ID NO.2+SEQ ID NO.1；
SEQ ID NO.1+SEQ ID NO.2+SEQ ID NO.1；SEQ ID NO.2+SEQ ID NO.1+SEQ ID NO.2.
By the excavation of serum microRNAs and biochemical indicator, present invention demonstrates that, by detecting serum microRNAs tables
Up to level, the prediction to sleep deprivation physiological effect individual difference can be realized, to which screening is more resistant to of sleep deprivation
Body.
What screening obtained in the present invention has forecast function to sleep deprivation physiological effect serotonin (5-HT) variation
MicroRNAs, higher with the correlation of sleep deprivation physiological regulating control, this is found to be system and discloses sleep deprivation influence human body
Molecular mechanism provides completely new clue, is worth further investigation.
Description of the drawings
Fig. 1 people's blood serotonin (5-HT) content detection standard curve.
Specific implementation mode
The present invention provides serotonin (5-HT) the correlation microRNAs markers hsa-miR- under sleep deprivation
4701-3p and/or hsa-miR-4800-5p, and the method using serotonin (5-HT) variation under its prediction sleep deprivation
The method that the shadow is rung with prediction sleep deprivation.
Although the present invention, which is regardless of, is limited to any theory, inventor thinks hsa-miR-4701-3p and hsa-miR-4800-
There are a kind of negative regulation relationships between the expression and serotonin (5-HT) of 5p.
In the present invention, inventor has found that serotonin (5-HT) variation can be expressed as Y=ax1+bx2+c, wherein x1
It is the regression coefficient that miRNA variables hsa-miR-4701-3p and hsa-miR-4800-5p, a and b are variable respectively with x2, c is
Constant.Also, inventor is it has furthermore been found that preferably there is such proportionate relationship a in a, b and c:b:C=(- 1):(- 12 to -8):
(20 to 30).For example, a, b and c are -0.1, -1 and 2.4 respectively.
Embodiment
1, sleep deprivation physiological effect model
(1) volunteer
Screen clinical physical examination health, totally 12 people, male, age are 20 to 50 to the volunteer of mental health.It will be above-mentioned
12 volunteers are randomly divided into 4 groups, every group of 3 people.Experiment requires the work and rest of every volunteer's holding rule for preceding 1 week, and (23 up to the 2nd
It 7 when arrange sleep).
(2) experiment condition
Sleep deprivation, when a length of 72h.Since subject terminate to add up 72h without sleep entering isolating chamber to experiment, works as quilt
It takes temperature and is waken up with a start with the tinkle of bells when revealing sleepy drowsiness.
(3) experimental arrangement
Third day before experiment, tests the emotional state of all volunteers, basic physiology biochemical indicator, as aspiration
The baseline value of person's These parameters.The emotional state of volunteer is tested using anxiety and Depression Scale and POMS questionnaires etc..Using dynamic
State physiological parameter records detector and tests volunteer's physiological indexes, acquires the morning 8 on the same day:00, noon 12:00, afternoon
16:00 and at night 20:00 saliva, urine analyze volunteer's biochemical indicator.Serotonin (5-HT) is tested, is exempted from using enzyme-linked
Epidemic disease determining adsorption (ELISA) universal method and kit.
Third day before experiment, acquisition morning 8 on the same day:00 blood, separation serum (1ml) are added RNA degradation protective agents and freeze
It deposits, remains follow-up microRNAs chip analysis.
Experiment terminates after volunteer fully rests the same day, is carried out to the emotional state of volunteer, basic physiology biochemical indicator
Test, the result after being tested as volunteer.Test method and index are the same as test before experiment.
2, data acquire general status
It is as shown in the table that data acquire situation：
1 data of table acquire situation table
3, serotonin (5-HT) content assaying method and quality control
According to people's serotonin (5-HT) ELISA kit (E01H0106, upper sea blue base biology) specification to 5-HT into
Row test.
To ensure the accuracy of data, double standard curves are all done on every piece of ELISA Plate, have been added in sample-adding process mid-term
Sample operates, and reduces error caused by loading time.Standard curve the result is shown in Figure 1.
Curve matching uses Curve Expert expert data analysis softwares, selects fitting degree highest MMF model logarithms
According to being analyzed.MMF model curve Fitting Analysis results are as follows in people's blood serotonin (5-HT) content detection：
MMF models:Y=(a*b+c*x^d)/(b+x^d)
Coefficient data:
A=4.82E-02
B=6.60E+04
C=2.03E+00
D=1.68E+00
MMF models:Y=(a*b+c*x^d)/(b+x^d)
Standard deviation:0.0731429
Related coefficient:0.9955208
Annotation:
It has been more than 100 repeat counts；
The fitting fails to converge to poor 0.000001 (CHI2 0.010700) of limit；
Weighting is not used.
For subject, 5-HT index collections are in two times of sleep deprivation " front/rear ", by subject according to index
" rise/fall " classifies.
The variation tendency of subject's 5-HT indexs is as shown in table 2.
The variation tendency of 2 5-HT indexs of table
4, microRNAs is tested
Using mankind's microRNAs chips of Agilent companies.
In view of biochip technology relative maturity, and there is the commercial technologies service platform of operation maturation in the country, using outer
The mode of association's test completes microRNAs chip testings.Main technologies include：
(1) Total RNAs extraction.Use commercial kit mirVanaTM RNA Isolation Kit (Applied
Biosystem p/n AM1556) extraction total serum IgE.
(2) RNA quality inspections.Using Bio Analyzer RNA6000Nano kit quality inspections, RIN=RNA is calculated
The quality inspections parameter such as Integrity Number.
(3) sample label and hybridization, using Agilent miRNA Complete Labeling and Hyb Kit.
(4) chip scanning is controlled using Agilent chip scanners with Agilent Scan Control software
System.
(5) data prediction and analysis, using GeneSpring GX softwares.
Above-mentioned experimental implementation is carried out by the standard practice instructions of related kit and company.
5, microRNAs chip datas pre-process
In the Agilent chip data output files of standard, it is undetected signal that original value (raw), which is 0.1,
(Not Detected).To all 12 samples, it is normalized after taking log2 with TotalProbeSignal.raw values, method
For quantile normal state (quantile normalization), tool is the analysis bag normalize.quantiles in R
{preprocessCore}.The miR probes of the equal no signal in 12 subjects are filtered out, remaining 373 probes, as follow-up
The input of analysis.
6, sleep deprivation effect forecast microRNAs markers screen
(1) sorting algorithm
Based on this project data feature, the multivariate regression models based on Lasso methods is selected to classify.The algorithm passes through
Punish that discriminant function carries out restrict to the regression coefficient of all variables, some meaningless or minimum meaning independents variable
Coefficient is compressed to 0, so that output function L (F) is reached maximum value, filters out most significant model variable.Training sample is carried out
100 random sampling with replacement training obtain 100 groups of optimal characteristics combinations, and all features are sorted according to the frequency of occurrences, are extracted
Existing frequency be more than 50% feature as optimal characteristics set.The model accuracy trained every time using 4 times of closs validation evaluations, and
Record the optimal characteristics collection that each training pattern filters out, the AUC areas of computation model.The ratio of training set and inspection set selects
It is 3:1, i.e., 9 are randomly selected from 12 samples every time and does training, 3 are examined, to assess classification estimated performance.
(2) 5-HT stress index microRNAs markers screening
It is turned to the criteria for classifying with the change of 5-HT indexs, distinguishes response big (sensitivity), small (insensitive) two classes of response
Crowd, by sorting algorithm screening for 5-HT stress index microRNAs markers.By sorting algorithm, filter out with
The maximally related microRNAs markers of 5-HT, hsa-miR-4701-3p and hsa-miR-4800-5p.It can be with from the selection result
Find out that there are a kind of negative regulation relationships between the expression and 5-HT of hsa-miR-4701-3p and hsa-miR-4800-5p, i.e.,
5-HT is horizontal after the high expression of hsa-miR-4701-3p and hsa-miR-4800-5p may lead to sleep deprivation before sleep deprivation
Reduction.The selection result is as shown in table 2.Prediction model coefficient such as table 3.Calculation formula used is as follows：
Y (5-ht)=- 0.1*hsa-miR-4701-3p-1*hsa-miR-4800-5p+2.4.According to the above results, consider
To the deviation between Different Individual, inventor provides following a, b and c proportionate relationship a:b:C=(- 1):(- 12 to -8):(20
To 30).
Table 2 there is the microRNAs of predictive ability to gather biochemical indicator
| BioChem | microRNAs | AUC |
| n_5HT | hsa-miR-4701-3p；hsa-miR-4800-5p | 100% |
3 prediction model coefficient of table
(3) 5-HT indexs correlation microRNAs target gene functions enrichment analysis
The 5-HT filtered out for sorting algorithm stress the microRNAs markers of index carry out function enrichment analysis.Table 4
Middle analysis result shows microRNAs target genes in the distribution situation of each tissue, and the tissue being most enriched with is brain etc..The result
Illustrate that these are mainly expressed in the brain with the relevant microRNAs markers of 5-HT, therefore these can be passed through
MicroRNAs markers are predicted and the relevant situation of brain, such as physiological status.
The distribution of the microRNAs target genes of 4 biochemical indicator 5-HT variation predictions of table in the tissue
| Tissue | It counts | % | P values |
| Brain | 164 | 49.54683 | 2.96E-04 |
| Colon | 31 | 9.365559 | 0.013665 |
| Duodenum | 8 | 2.416918 | 0.015992 |
| Small intestine | 4 | 1.208459 | 0.030403 |
| Fetal kidney | 8 | 2.416918 | 0.034825 |
(1)-(3) result illustrates, high express of hsa-miR-4701-3p and hsa-miR-4800-5p is led before sleep deprivation
The reduction of 5-HT after cause sleep deprivation, and hsa-miR-4701-3p and hsa-miR-4800-5p are mainly expressed in the brain, because
This can predict in brain that 5-HT is conditions associated (such as raw by the variation of hsa-miR-4701-3p and hsa-miR-4800-5p
Reason state) variation, play the role of early warning to the appearance of this situation.Inventor is also tested for SEQ ID NO.1+SEQ ID
NO.2, SEQ ID NO.2+SEQ ID NO.1, SEQ ID NO.1+SEQ ID NO.2+SEQ ID NO.1 and SEQ ID NO.2
+ SEQ ID NO.1+SEQ ID NO.2 and its complementary series by hybridizing method detect in tissue hsa-miR-4701-3p and
The effect of hsa-miR-4800-5p finds that they could be used for detection hsa-miR-4701-3p and hsa-miR-4800-5p bis-
The qualitative variation of person, wherein with SEQ ID NO.1+SEQ ID NO.2, SEQ ID NO.2+SEQ ID NO.1 effect the most
It is ideal.Therefore, if to not specially requiring quantitatively, they may be used to qualitatively predict serotonin under sleep deprivation
(5-HT) changes, to predict the variation of 5-HT conditions associated (such as physiological status) in brain.
Claims (6)
1. serotonin under a kind of prediction sleep deprivation of non-diagnostic purpose（5-HT）The method of variation, the method includes detections
The level of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p expression, hsa-miR-4701-3p and
The sequence of hsa-miR-4800-5p is SEQ ID NO.1 and SEQ ID NO.2, the hsa-miR-4701- of high expression level respectively
3p and hsa-miR-4800-5p indicates serotonin after sleep deprivation（5-HT）Horizontal reduction.
2. according to the method described in claim 1, serotonin（5-HT）It is variable miRNA to change Y=ax1+bx2+c, x1 and x2
Marker hsa-miR-4701-3p and hsa-miR-4800-5p, a and b are the regression coefficients of variable, and c is constant, a:b:c =
(-1):(- 12 to -8):(20 to 30).
3. according to the method described in claim 2, a and b are -0.1 and -1 respectively, c 2.4.
4. the method that a kind of prediction sleep deprivation of non-diagnostic purpose rings the shadow, the method includes detection microRNAs marks
The level of will object hsa-miR-4701-3p and hsa-miR-4800-5p expression, hsa-miR-4701-3p and hsa-miR-4800-
The sequence of 5p is SEQ ID NO.1 and SEQ ID NO.2, the hsa-miR-4701-3p and hsa-miR- of high expression level respectively
4800-5p indicate to sleep deprivation more resistant to.
5.microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p are used to prepare 5- under prediction sleep deprivation
Hydroxytryptamine（5-HT）The purposes for the kit that variation or sleep deprivation ring the shadow, the kit includes hsa-miR-4701-
3p and hsa-miR-4800-5p, wherein the sequence of the hsa-miR-4701-3p and hsa-miR-4800-5p is SEQ respectively
ID NO.1 and SEQ ID NO.2.
6. purposes according to claim 5, the kit further includes DNA and/or RNA detection reagents.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104039960A (en) * | 2011-08-04 | 2014-09-10 | 耶达研究及发展有限公司 | Micro-rnas and compositions comprising same for the treatment and diagnosis of serotonin-, adrenalin-, noradrenalin-, glutamate-, and corticotropin-releasing hormone- associated medical conditions |
| WO2014192907A1 (en) * | 2013-05-30 | 2014-12-04 | 国立大学法人東京医科歯科大学 | Microrna detection method used to differentiate disease exhibiting motor nerve disability |
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| CN104039960A (en) * | 2011-08-04 | 2014-09-10 | 耶达研究及发展有限公司 | Micro-rnas and compositions comprising same for the treatment and diagnosis of serotonin-, adrenalin-, noradrenalin-, glutamate-, and corticotropin-releasing hormone- associated medical conditions |
| WO2014192907A1 (en) * | 2013-05-30 | 2014-12-04 | 国立大学法人東京医科歯科大学 | Microrna detection method used to differentiate disease exhibiting motor nerve disability |
Non-Patent Citations (1)
| Title |
|---|
| sleep loss changes microRNA levels in the brain: a possible mechanism for state-dependent translational regulation;Christopher J. Davis等;《neuroscience letters》;20071231;第422卷;68-73 * |
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